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Media regarding COVID-19 fatality counts is crucial, affecting policy and health measures nationwide. However, misinformation regarding other causes of death has led to dubious claims about the ser...

The study of location and location-based phenomena is a flourishing field. Many universities have grown their research and/or services in this field (often called GIS), established centers that are...

The United Nations Sustainable Development Goals (SDGs) include 17 interlinked goals designed to be a blueprint for the world’s nations to achieve a better and more sustainable future, and the specific SDG 3 is a public health–related goal to ensure healthy living and promote well-being for all population groups. To facilitate SDG planning, implementation, and progress monitoring, many SDG indicators have been developed. Based on the United Nations General Assembly resolutions, SDG indicators need to be disaggregated by geographic locations and thematic environmental and socioeconomic characteristics for achieving the most accurate planning and progress assessment. High-resolution data such as those captured at the village level can provide comparatively more precise insights into the different socioeconomic and environmental factors relevant to SDGs, therefore enabling more effective sustainable development decision-making. Using India as our study area and the child malnutrition indicators stunting, underweight, and wasting as examples of public health–related SDG indicators, we have demonstrated a process to effectively derive environmental variables at the village level from satellite big datasets on a cloud platform for SDG research and applications. Spatial analysis of environmental variables regarding vegetation, climate, and terrain have shown spatial grouping patterns across the entire study area, with each village group having different statistics. Correlation analysis between these environmental variables and stunting, underweight, and wasting indicators show a meaningful relationship between these indicators and vegetation index, land surface temperature, rainfall, elevation, and slope. Identifying the spatial variation patterns of environmental variables at the village level and their correlations with child malnutrition indicators can be an invaluable tool to facilitate a clearer understanding of the causes of child malnutrition and to improve area-specific SDG 3 implementation planning. This analysis can also provide meaningful support in assessing and monitoring SDG implementation progress at the village level by spatially predicting SDG indicators using available socioeconomic and environmental independent variables. The methodology used in this study has the potential to be applied to other similar regions, especially low-to-middle income countries where a high number of children are severely affected by malnutrition, as well as to other environmentally related SDGs, such as Goal 1 (No Poverty) and Goal 2 (Zero Hunger).

The U.S. has merely 4% of the world population, but contains 25% of the world&rsquo, s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.

WorldMap is an open source online mapping application which aims to lower barriers for scholars who wish to visualize, analyze, organize, present, and publish mapped information. In late 2013, 290 respondents among the 8,000 registered users participated in an online survey in which they described their activities, purposes, experiences, and preferences regarding the system. Participants also described their professional background, GIS skill level, age, gender, and country of work. This study analyzes the results of the survey, by summarizing the responses to each question independently and by examining the relationships and dependencies of these answers across the different questions to try to better understand why users responded the way they did. The study is based on the user-centered design (UCD) approach. We aim to use the survey results to improve our understanding of user demographics and needs. Findings from this study will be used to guide WorldMap improvements, and we hope the findings will also shed light on the broader requirements of online GIS users.

The goal of this chapter is to evaluate and compare Geospatial Software as a Service (GSaaS) platforms oriented toward providing basic mapping capabilities to non-GIS experts. These platforms allow users to organize spatial materials in layers, perform overlay and basic visual analysis, and share both final maps and the processes used to create them with remote collaborators. The authors gathered data on the characteristics of 15 platforms through an online survey, then summarized the results and created an Excel tool to enable users to sift through the data to identify platforms based on need. This study presents a snapshot of the current GSaaS landscape, summarizes current capabilities, points out weaknesses, and considers the potential of this class of application.

WorldMap is a web-based, map-centric data exploration system built on open-source geospatial technology at Harvard University. It is designed to serve collaborative research and teaching, but is also accessible to the general public. This article explains WorldMap's basic functions through several historical research projects, demonstrating its flexible scale (from neighborhood to continent) and diverse research themes (social, political, economic, cultural, infrastructural, etc.). Also shared in this article are our experiences in handling technical and institutional challenges during system development, such as synchronization of software components being developed by multiple organizations; juggling competing priorities for serving individual requests and developing a system that will enable users to support themselves; balancing promotion of the system usage with constraints on infrastructure investment; harnessing volunteered geographic information while managing data quality; as well as protecting c...

The Center for Geographic Analysis (CGA) at Harvard University supports research and teaching that relies on geographic information. This includes supporting geographic analysis for public health research at Harvard. This article reviews geographic concepts that apply to public health, pertinent data available in geographic format, and GIS analytical techniques. The work-flow methodology the CGA has developed for conducting research with geographic data will be presented, highlighting successful practices to follow and pitfalls to avoid. Applications of this work flow are illustrated through an in-depth discussion of specific case studies in public health research at the university.

Founded in 1818, the Harvard Map Collection (HMC) is the oldest map collection in America, holding 400,000 maps, more than 6,000 atlases, and thousands of reference books. HMC has a strong commitment to digital resources, and it manages the Harvard Geospatial Library, a foundation for geospatial data service at Harvard. The Center for Geographic Analysis (CGA) at Harvard University was founded in 2006, independent of the library system, to serve the entire university. This article presents the history, organizational structure, and operational model of CGA and HMC, reviews achievements, lessons learned, suggests future improvements, and reviews GIS-related medical research at Harvard.

Without a department of geography, Harvard University established the Center for Geographic Analysis (CGA) in 2006 to support research and teaching of all disciplines across the University with emerging geospatial technologies. In the past four and a half years, CGA built an institutional service infrastructure and unleashed an increasing demand on geographic analysis in many fields. CGA services range from helpdesk, project consultation, training, hardware/software administration, community building, to system development and methodology research. Services often start as an application of existing GIS technology, eventually contributing to the study of geographic information science in many ways. As a new generation of students and researchers growing up with Google Earth and the like, their demand for geospatial services will continue to push CGA into new territories.

This paper will detail how the Center for Geographic Analysis, Harvard Map Collection, and Harvard Geospatial Library work together to enable medical and public health research across Harvard University. It will discuss geographic concepts that apply to medicine and public health, pertinent data available in geographic format, and analytical techniques. The workflow methodology the CGA has developed for conducting medical research with geographic data will be presented, highlighting successful practices to follow and pitfalls to avoid. Applications of this workflow will be illustrated through the documentation of specific projects in medical and public health research at the University.